Dynamic Reporting

Overview

1. What is dynamic reporting?

2. How to do dynamic reporting?

3. Hands-on exercises

Manual reporting workflow

What are the disadvantages?

Dynamic reporting workflow

Popular tools for dynamic reporting

Which tool for whom?

• knitr → R+LaTeX users (intermediate to advanced)
• R Markdown → R users (beginner to advanced)
• Quarto → R/Python/Julia users (beginner to advanced)

Useful links

• Dynamic Reporting & Reproducibility in Research
  → transdisciplinary course at UZH

• Dynamic Reporting Primer
  → Primer article from the CRS

• R Markdown tutorial
  → good tutorial series from R Studio

• R Markdown: The Definitive Guide
  → freely available book

• knitr website
  → very comprehensive website from the knitr author

• Quarto tutorial
  → good tutorial series from R Studio

• R Markdown cheatsheet

Exercises

1. Download the data sets on traffic accidents in Zurich from https://github.crsuzh/dynamicReporting/workshop/.

2. Explore the data from the 2020 (file accidents_2020.csv) . Compute the number of accidents for each weekday and visualize your results. Create an HTML report using either R Markdown or Quarto.

3. Now use the data from 2020 and 2021 (file accidents_2021.csv) and create an updated report.

4. Bonus: Convert your report to a presentation.

Backup: R Markdown

• R programming language
  (> 60 other also possible)

• .Rmd files (edit in RStudio, VS Code, etc.)

• Markup language: Markdown

• HTML, PDF, DOCX output formats (and more)

• rmarkdown is an R package

Backup: knitr

• Programming language: R
  (> 60 other also possible)

• .Rnw files (edit in RStudio, VS Code, etc.)

• Markup languages: LaTeX
  (+ HTML, Markdown, and more)

• PDF output format (and more)

• knitr is an R package

Backup: Quarto

• Programming language: R, Python, Julia

• .qmd files (edit in RStudio, VS Code, etc.)

• Markup language: Markdown

• HTML, PDF, DOCX output formats (and more)

• Evolution of R Markdown (introduced in 2022)

• quarto is a separate program